{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>133</td>\n",
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       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  rating  timestamp\n",
       "0        0       50       5  881250949\n",
       "1        0      172       5  881250949\n",
       "2        0      133       1  881250949\n",
       "3      196      242       3  881250949\n",
       "4      186      302       3  891717742"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取电影评分表\n",
    "df = pd.read_csv('data/用户-电影评分表.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id              title\n",
       "0        1   Toy Story (1995)\n",
       "1        2   GoldenEye (1995)\n",
       "2        3  Four Rooms (1995)\n",
       "3        4  Get Shorty (1995)\n",
       "4        5     Copycat (1995)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取电影表\n",
    "movie_titles = pd.read_csv(\"data/电影id对应表.csv\")\n",
    "movie_titles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Star Wars (1977)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>880473582</td>\n",
       "      <td>Star Wars (1977)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>891271545</td>\n",
       "      <td>Star Wars (1977)</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>888552084</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>879362124</td>\n",
       "      <td>Star Wars (1977)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  rating  timestamp             title\n",
       "0        0       50       5  881250949  Star Wars (1977)\n",
       "1      290       50       5  880473582  Star Wars (1977)\n",
       "2       79       50       4  891271545  Star Wars (1977)\n",
       "3        2       50       5  888552084  Star Wars (1977)\n",
       "4        8       50       5  879362124  Star Wars (1977)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过“item_id”字段，连接评分表和电影表\n",
    "\n",
    "df = pd.merge(df,movie_titles,on='item_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "They Made Me a Criminal (1939)                5.0\n",
       "Marlene Dietrich: Shadow and Light (1996)     5.0\n",
       "Saint of Fort Washington, The (1993)          5.0\n",
       "Someone Else's America (1995)                 5.0\n",
       "Star Kid (1997)                               5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].mean().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Star Wars (1977)             584\n",
       "Contact (1997)               509\n",
       "Fargo (1996)                 508\n",
       "Return of the Jedi (1983)    507\n",
       "Liar Liar (1997)             485\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('title')['rating'].count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_rating</th>\n",
       "      <th>count_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>2.908257</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.344000</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>3.024390</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean_rating  count_rating\n",
       "title                                               \n",
       "'Til There Was You (1997)     2.333333             9\n",
       "1-900 (1994)                  2.600000             5\n",
       "101 Dalmatians (1996)         2.908257           109\n",
       "12 Angry Men (1957)           4.344000           125\n",
       "187 (1997)                    3.024390            41"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings_series = df.groupby('title')['rating'].mean()\n",
    "count_ratings_series = df.groupby('title')['rating'].count()\n",
    "\n",
    "# 将两个 Series 合并为一个 DataFrame\n",
    "ratings_df = pd.DataFrame({'mean_rating': mean_ratings_series, 'count_rating': count_ratings_series})\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "ratings_df['count_rating'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "ratings_df['mean_rating'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2398d011bd0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 400x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "sns.jointplot(x='mean_rating',y='count_rating',\n",
    "              data=ratings_df,alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <th>187 (1997)</th>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
       "      <th>2001: A Space Odyssey (1968)</th>\n",
       "      <th>3 Ninjas: High Noon At Mega Mountain (1998)</th>\n",
       "      <th>39 Steps, The (1935)</th>\n",
       "      <th>...</th>\n",
       "      <th>Yankee Zulu (1994)</th>\n",
       "      <th>Year of the Horse (1997)</th>\n",
       "      <th>You So Crazy (1994)</th>\n",
       "      <th>Young Frankenstein (1974)</th>\n",
       "      <th>Young Guns (1988)</th>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <th>Young Poisoner's Handbook, The (1995)</th>\n",
       "      <th>Zeus and Roxanne (1997)</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Á köldum klaka (Cold Fever) (1994)</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>939</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>944 rows × 1664 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title    'Til There Was You (1997)  1-900 (1994)  101 Dalmatians (1996)  \\\n",
       "user_id                                                                   \n",
       "0                              NaN           NaN                    NaN   \n",
       "1                              NaN           NaN                    2.0   \n",
       "2                              NaN           NaN                    NaN   \n",
       "3                              NaN           NaN                    NaN   \n",
       "4                              NaN           NaN                    NaN   \n",
       "...                            ...           ...                    ...   \n",
       "939                            NaN           NaN                    NaN   \n",
       "940                            NaN           NaN                    NaN   \n",
       "941                            NaN           NaN                    NaN   \n",
       "942                            NaN           NaN                    NaN   \n",
       "943                            NaN           NaN                    NaN   \n",
       "\n",
       "title    12 Angry Men (1957)  187 (1997)  2 Days in the Valley (1996)  \\\n",
       "user_id                                                                 \n",
       "0                        NaN         NaN                          NaN   \n",
       "1                        5.0         NaN                          NaN   \n",
       "2                        NaN         NaN                          NaN   \n",
       "3                        NaN         2.0                          NaN   \n",
       "4                        NaN         NaN                          NaN   \n",
       "...                      ...         ...                          ...   \n",
       "939                      NaN         NaN                          NaN   \n",
       "940                      NaN         NaN                          NaN   \n",
       "941                      NaN         NaN                          NaN   \n",
       "942                      NaN         NaN                          NaN   \n",
       "943                      NaN         NaN                          2.0   \n",
       "\n",
       "title    20,000 Leagues Under the Sea (1954)  2001: A Space Odyssey (1968)  \\\n",
       "user_id                                                                      \n",
       "0                                        NaN                           NaN   \n",
       "1                                        3.0                           4.0   \n",
       "2                                        NaN                           NaN   \n",
       "3                                        NaN                           NaN   \n",
       "4                                        NaN                           NaN   \n",
       "...                                      ...                           ...   \n",
       "939                                      NaN                           NaN   \n",
       "940                                      NaN                           NaN   \n",
       "941                                      NaN                           NaN   \n",
       "942                                      NaN                           3.0   \n",
       "943                                      NaN                           NaN   \n",
       "\n",
       "title    3 Ninjas: High Noon At Mega Mountain (1998)  39 Steps, The (1935)  \\\n",
       "user_id                                                                      \n",
       "0                                                NaN                   NaN   \n",
       "1                                                NaN                   NaN   \n",
       "2                                                1.0                   NaN   \n",
       "3                                                NaN                   NaN   \n",
       "4                                                NaN                   NaN   \n",
       "...                                              ...                   ...   \n",
       "939                                              NaN                   NaN   \n",
       "940                                              NaN                   NaN   \n",
       "941                                              NaN                   NaN   \n",
       "942                                              NaN                   3.0   \n",
       "943                                              NaN                   NaN   \n",
       "\n",
       "title    ...  Yankee Zulu (1994)  Year of the Horse (1997)  \\\n",
       "user_id  ...                                                 \n",
       "0        ...                 NaN                       NaN   \n",
       "1        ...                 NaN                       NaN   \n",
       "2        ...                 NaN                       NaN   \n",
       "3        ...                 NaN                       NaN   \n",
       "4        ...                 NaN                       NaN   \n",
       "...      ...                 ...                       ...   \n",
       "939      ...                 NaN                       NaN   \n",
       "940      ...                 NaN                       NaN   \n",
       "941      ...                 NaN                       NaN   \n",
       "942      ...                 NaN                       NaN   \n",
       "943      ...                 NaN                       NaN   \n",
       "\n",
       "title    You So Crazy (1994)  Young Frankenstein (1974)  Young Guns (1988)  \\\n",
       "user_id                                                                      \n",
       "0                        NaN                        NaN                NaN   \n",
       "1                        NaN                        5.0                3.0   \n",
       "2                        NaN                        NaN                NaN   \n",
       "3                        NaN                        NaN                NaN   \n",
       "4                        NaN                        NaN                NaN   \n",
       "...                      ...                        ...                ...   \n",
       "939                      NaN                        NaN                NaN   \n",
       "940                      NaN                        NaN                NaN   \n",
       "941                      NaN                        NaN                NaN   \n",
       "942                      NaN                        NaN                NaN   \n",
       "943                      NaN                        NaN                4.0   \n",
       "\n",
       "title    Young Guns II (1990)  Young Poisoner's Handbook, The (1995)  \\\n",
       "user_id                                                                \n",
       "0                         NaN                                    NaN   \n",
       "1                         NaN                                    NaN   \n",
       "2                         NaN                                    NaN   \n",
       "3                         NaN                                    NaN   \n",
       "4                         NaN                                    NaN   \n",
       "...                       ...                                    ...   \n",
       "939                       NaN                                    NaN   \n",
       "940                       NaN                                    NaN   \n",
       "941                       NaN                                    NaN   \n",
       "942                       NaN                                    NaN   \n",
       "943                       3.0                                    NaN   \n",
       "\n",
       "title    Zeus and Roxanne (1997)  unknown  Á köldum klaka (Cold Fever) (1994)  \n",
       "user_id                                                                        \n",
       "0                            NaN      NaN                                 NaN  \n",
       "1                            NaN      4.0                                 NaN  \n",
       "2                            NaN      NaN                                 NaN  \n",
       "3                            NaN      NaN                                 NaN  \n",
       "4                            NaN      NaN                                 NaN  \n",
       "...                          ...      ...                                 ...  \n",
       "939                          NaN      NaN                                 NaN  \n",
       "940                          NaN      NaN                                 NaN  \n",
       "941                          NaN      NaN                                 NaN  \n",
       "942                          NaN      NaN                                 NaN  \n",
       "943                          NaN      NaN                                 NaN  \n",
       "\n",
       "[944 rows x 1664 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviemat = df.pivot_table(index='user_id',columns='title',values='rating')\n",
    "moviemat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 我们选择两部电影，科幻电影-星球大战（Star Wars），喜剧电影（Liar Liar）\n",
    "starwars_user_ratings = moviemat['Star Wars (1977)']\n",
    "liarliar_user_ratings = moviemat['Liar Liar (1997)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "0    5.0\n",
       "1    5.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    5.0\n",
       "Name: Star Wars (1977), dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starwars_user_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "0    NaN\n",
       "1    NaN\n",
       "2    1.0\n",
       "3    2.0\n",
       "4    5.0\n",
       "Name: Liar Liar (1997), dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liarliar_user_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WX847\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2889: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar, dtype=dtype)\n",
      "C:\\Users\\WX847\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2748: RuntimeWarning: divide by zero encountered in divide\n",
      "  c *= np.true_divide(1, fact)\n",
      "C:\\Users\\WX847\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2889: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar, dtype=dtype)\n",
      "C:\\Users\\WX847\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2748: RuntimeWarning: divide by zero encountered in divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    }
   ],
   "source": [
    "similar_to_starwars = moviemat.corrwith(starwars_user_ratings)\n",
    "similar_to_liarliar = moviemat.corrwith(liarliar_user_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
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       "      <th>1-900 (1994)</th>\n",
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       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.211132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.184289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187 (1997)</th>\n",
       "      <td>0.027398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Correlation\n",
       "title                                 \n",
       "'Til There Was You (1997)     0.872872\n",
       "1-900 (1994)                 -0.645497\n",
       "101 Dalmatians (1996)         0.211132\n",
       "12 Angry Men (1957)           0.184289\n",
       "187 (1997)                    0.027398"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars = pd.DataFrame(similar_to_starwars,columns=['Correlation'])\n",
    "corr_starwars.dropna(inplace=True)\n",
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hollow Reed (1996)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stripes (1981)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Star Wars (1977)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man of the Year (1995)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beans of Egypt, Maine, The (1994)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Safe Passage (1994)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlaw, The (1943)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Line King: Al Hirschfeld, The (1996)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hurricane Streets (1998)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation\n",
       "title                                                          \n",
       "Hollow Reed (1996)                                          1.0\n",
       "Stripes (1981)                                              1.0\n",
       "Star Wars (1977)                                            1.0\n",
       "Man of the Year (1995)                                      1.0\n",
       "Beans of Egypt, Maine, The (1994)                           1.0\n",
       "Safe Passage (1994)                                         1.0\n",
       "Old Lady Who Walked in the Sea, The (Vieille qu...          1.0\n",
       "Outlaw, The (1943)                                          1.0\n",
       "Line King: Al Hirschfeld, The (1996)                        1.0\n",
       "Hurricane Streets (1998)                                    1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars.sort_values('Correlation',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count_rating</th>\n",
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       "    <tr>\n",
       "      <th>title</th>\n",
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       "      <th></th>\n",
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       "      <th>'Til There Was You (1997)</th>\n",
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       "      <th>1-900 (1994)</th>\n",
       "      <td>-0.645497</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>0.211132</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
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       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>0.184289</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
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       "      <th>187 (1997)</th>\n",
       "      <td>0.027398</td>\n",
       "      <td>41</td>\n",
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      ],
      "text/plain": [
       "                           Correlation  count_rating\n",
       "title                                               \n",
       "'Til There Was You (1997)     0.872872             9\n",
       "1-900 (1994)                 -0.645497             5\n",
       "101 Dalmatians (1996)         0.211132           109\n",
       "12 Angry Men (1957)           0.184289           125\n",
       "187 (1997)                    0.027398            41"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars = corr_starwars.join(ratings_df['count_rating'])\n",
    "corr_starwars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Star Wars (1977)</th>\n",
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       "      <td>584</td>\n",
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       "    <tr>\n",
       "      <th>Empire Strikes Back, The (1980)</th>\n",
       "      <td>0.748353</td>\n",
       "      <td>368</td>\n",
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       "    <tr>\n",
       "      <th>Return of the Jedi (1983)</th>\n",
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       "      <td>507</td>\n",
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       "      <th>Raiders of the Lost Ark (1981)</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin Powers: International Man of Mystery (1997)</th>\n",
       "      <td>0.377433</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Correlation  count_rating\n",
       "title                                                                        \n",
       "Star Wars (1977)                                       1.000000           584\n",
       "Empire Strikes Back, The (1980)                        0.748353           368\n",
       "Return of the Jedi (1983)                              0.672556           507\n",
       "Raiders of the Lost Ark (1981)                         0.536117           420\n",
       "Austin Powers: International Man of Mystery (1997)     0.377433           130"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_starwars[corr_starwars['count_rating']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation</th>\n",
       "      <th>count_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Liar Liar (1997)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Batman Forever (1995)</th>\n",
       "      <td>0.516968</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mask, The (1994)</th>\n",
       "      <td>0.484650</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Down Periscope (1996)</th>\n",
       "      <td>0.472681</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Con Air (1997)</th>\n",
       "      <td>0.469828</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Correlation  count_rating\n",
       "title                                           \n",
       "Liar Liar (1997)          1.000000           485\n",
       "Batman Forever (1995)     0.516968           114\n",
       "Mask, The (1994)          0.484650           129\n",
       "Down Periscope (1996)     0.472681           101\n",
       "Con Air (1997)            0.469828           137"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_liarliar = pd.DataFrame(similar_to_liarliar,columns=['Correlation'])\n",
    "corr_liarliar.dropna(inplace=True)\n",
    "corr_liarliar = corr_liarliar.join(ratings_df['count_rating'])\n",
    "corr_liarliar[corr_liarliar['count_rating']>100].sort_values('Correlation',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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